Toward a Rational and Mechanistic Account of Mental Effort.

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Title: Toward a Rational and Mechanistic Account of Mental Effort.
Authors: Shenhav, Amitai, Musslick, Sebastian, Lieder, Falk, Kool, Wouter, Griffiths, Thomas L., Cohen, Jonathan D., Botvinick, Matthew M.
Source: Annual Review of Neuroscience. Jul2017, Vol. 40, p99-124. 21p.
Subjects: Cognitive ability, Neural circuitry, Neurosciences, Prefrontal cortex, Motivation (Psychology), Executive function
Abstract: In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate mental effort to minimize those costs and maximize the attendant benefits. This review explores recent advances in computational modeling and empirical research aimed at addressing these questions at the level of psychological process and neural mechanism, examining both the limitations to mental effort exertion and how we manage those limited cognitive resources. We conclude by identifying remaining challenges for theoretical accounts of mental effort as well as possible applications of the available findings to understanding the causes of and potential solutions for apparent failures to exert the mental effort required of us. [ABSTRACT FROM AUTHOR]
Copyright of Annual Review of Neuroscience is the property of Annual Reviews Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="DE" term="%22Cognitive+ability%22">Cognitive ability</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+circuitry%22">Neural circuitry</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosciences%22">Neurosciences</searchLink><br /><searchLink fieldCode="DE" term="%22Prefrontal+cortex%22">Prefrontal cortex</searchLink><br /><searchLink fieldCode="DE" term="%22Motivation+%28Psychology%29%22">Motivation (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Executive+function%22">Executive function</searchLink>
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  Data: In spite of its familiar phenomenology, the mechanistic basis for mental effort remains poorly understood. Although most researchers agree that mental effort is aversive and stems from limitations in our capacity to exercise cognitive control, it is unclear what gives rise to those limitations and why they result in an experience of control as costly. The presence of these control costs also raises further questions regarding how best to allocate mental effort to minimize those costs and maximize the attendant benefits. This review explores recent advances in computational modeling and empirical research aimed at addressing these questions at the level of psychological process and neural mechanism, examining both the limitations to mental effort exertion and how we manage those limited cognitive resources. We conclude by identifying remaining challenges for theoretical accounts of mental effort as well as possible applications of the available findings to understanding the causes of and potential solutions for apparent failures to exert the mental effort required of us. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Annual Review of Neuroscience is the property of Annual Reviews Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1146/annurev-neuro-072116-031526
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        Text: English
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      – SubjectFull: Neural circuitry
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      – SubjectFull: Neurosciences
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              Text: Jul2017
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              Y: 2017
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